Warnakulasuriya, Kavindu (University of Moratuwa), Weerasinghe, Ravindi (University of Moratuwa), Wickramarathna, Gayani (University of Moratuwa), Ratneswaran, Shiveswarran (University of Moratuwa), Thayasivam, Uthayasanker (University of Moratuwa)

Explainable Bus Arrival Time Prediction Model with Improved Features Related to Topography and Points of Interest

Scheduled for presentation during the Poster Session "Travel Information, Travel Guidance, and Travel Demand Management" (ThAT16), Thursday, September 26, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Accurate and reliable prediction of bus arrival times enhances passenger mobility experience. This study addresses a significant research gap by focusing on the complexities of predicting bus arrival times in heterogeneous traffic conditions. Unlike conventional prediction models, this research identifies hidden features related to topographical and Points of Interest (POIs) data, recognizing their critical role in reasoning. The methodology involves a two-fold approach, segmenting predictions into running time within a segment and dwell time at bus halts, using the multi-model ensemble technique. The results indicate that incorporating the new features (5 topographical and 12 POIs-related) has improved model performance by a reduction in MAE of 1.37 seconds (dwell time) and a decrease in MAPE by 0.7% (running time). While the enhancements in accuracy may appear modest, our focus lies on examining the influence of new features, offering valuable insights into the factors that cause delays. Moreover, we developed a dashboard showcasing real-time bus arrival times and highlighting delay reasoning using explainable AI techniques.